The complexity of event-based object detection (OD) poses considerable
challenges. Spiking Neural Networks (SNNs) show promising results and pave the
way for efficient event-based OD. Despite this success, the path to efficient
SNNs on embedded devices remains a challenge. This is due to the size of the
networks required to accomplish the task and the ability of devices to take
advantage of SNNs benefits. Even when "edge" devices are considered, they
typically use embedded GPUs that consume tens of watts. In response to these
challenges, our research introduces an embedded neuromorphic testbench that
utilizes the SPiking Low-power Event-based ArchiTecture (SPLEAT) accelerator.
Using an extended version of the Qualia framework, we can train, evaluate,
quantize, and deploy spiking neural networks on an FPGA implementation of
SPLEAT. We used this testbench to load a state-of-the-art SNN solution,
estimate the performance loss associated with deploying the network on
dedicated hardware, and run real-world event-based OD on neuromorphic hardware
specifically designed for low-power spiking neural networks. Remarkably, our
embedded spiking solution, which includes a model with 1.08 million parameters,
operates efficiently with 490 mJ per prediction.

利用 SPLEAT 加速器的嵌入式神经形态测试台成功地在可嵌入设备上训练、评估、量化和部署了脉冲神经网络，通过加载最新的神经网络解决方案，估计了在专门设计的低功耗脉冲神经网络硬件上部署网络所带来的性能损失，并在神经形态硬件上实现了真实世界的基于事件的目标检测，令人惊叹的是，具有 108 万参数的嵌入式脉冲解决方案每个预测只需消耗 490 mJ。